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Merge pull request #480 from pipecat-ai/aleix/input-output-frames

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introduce input/output audio and image frames
This commit is contained in:
Aleix Conchillo Flaqué
2024-09-20 14:44:37 -07:00
committed by GitHub
parent 58d9c84bc9 7e39d9ad3d
commit 14acf05a26
48 changed files with 410 additions and 258 deletions
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9
CHANGELOG.md
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@@ -63,6 +63,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed
- We now distinguish between input and output audio and image frames. We
introduce `InputAudioRawFrame`, `OutputAudioRawFrame`, `InputImageRawFrame`
and `OutputImageRawFrame` (and other subclasses of those). The input frames
usually come from an input transport and are meant to be processed inside the
pipeline to generate new frames. However, the input frames will not be sent
through an output transport. The output frames can also be processed by any
frame processor in the pipeline and they are allowed to be sent by the output
transport.
- `ParallelTask` has been renamed to `SyncParallelPipeline`. A
`SyncParallelPipeline` is a frame processor that contains a list of different
pipelines to be executed concurrently. The difference between a

2
examples/dialin-chatbot/requirements.txt
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@@ -1,4 +1,4 @@
pipecat-ai[daily,openai,silero]
pipecat-ai[daily,elevenlabs,openai,silero]
fastapi
uvicorn
python-dotenv

12
examples/foundational/05a-local-sync-speech-and-image.py
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@@ -11,7 +11,13 @@ import sys
import tkinter as tk
from pipecat.frames.frames import AudioRawFrame, Frame, URLImageRawFrame, LLMMessagesFrame, TextFrame
from pipecat.frames.frames import (
Frame,
OutputAudioRawFrame,
TTSAudioRawFrame,
URLImageRawFrame,
LLMMessagesFrame,
TextFrame)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
@@ -65,9 +71,9 @@ async def main():
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, AudioRawFrame):
if isinstance(frame, TTSAudioRawFrame):
self.audio.extend(frame.audio)
self.frame = AudioRawFrame(
self.frame = OutputAudioRawFrame(
bytes(self.audio), frame.sample_rate, frame.num_channels)
class ImageGrabber(FrameProcessor):

13
examples/foundational/06a-image-sync.py
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@@ -11,7 +11,7 @@ import sys
from PIL import Image
from pipecat.frames.frames import ImageRawFrame, Frame, SystemFrame, TextFrame
from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
@@ -52,9 +52,16 @@ class ImageSyncAggregator(FrameProcessor):
await super().process_frame(frame, direction)
if not isinstance(frame, SystemFrame) and direction == FrameDirection.DOWNSTREAM:
await self.push_frame(ImageRawFrame(image=self._speaking_image_bytes, size=(1024, 1024), format=self._speaking_image_format))
await self.push_frame(OutputImageRawFrame(
image=self._speaking_image_bytes,
size=(1024, 1024),
format=self._speaking_image_format)
)
await self.push_frame(frame)
await self.push_frame(ImageRawFrame(image=self._waiting_image_bytes, size=(1024, 1024), format=self._waiting_image_format))
await self.push_frame(OutputImageRawFrame(
image=self._waiting_image_bytes,
size=(1024, 1024),
format=self._waiting_image_format))
else:
await self.push_frame(frame)

25
examples/foundational/09-mirror.py
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@@ -8,9 +8,11 @@ import aiohttp
import asyncio
import sys
from pipecat.frames.frames import Frame, InputAudioRawFrame, InputImageRawFrame, OutputAudioRawFrame, OutputImageRawFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.services.daily import DailyTransport, DailyParams
from runner import configure
@@ -24,6 +26,27 @@ logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
class MirrorProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, InputAudioRawFrame):
await self.push_frame(OutputAudioRawFrame(
audio=frame.audio,
sample_rate=frame.sample_rate,
num_channels=frame.num_channels)
)
elif isinstance(frame, InputImageRawFrame):
await self.push_frame(OutputImageRawFrame(
image=frame.image,
size=frame.size,
format=frame.format)
)
else:
await self.push_frame(frame, direction)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
@@ -44,7 +67,7 @@ async def main():
async def on_first_participant_joined(transport, participant):
transport.capture_participant_video(participant["id"])
pipeline = Pipeline([transport.input(), transport.output()])
pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
runner = PipelineRunner()

23
examples/foundational/09a-local-mirror.py
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@@ -10,9 +10,11 @@ import sys
import tkinter as tk
from pipecat.frames.frames import Frame, InputAudioRawFrame, InputImageRawFrame, OutputAudioRawFrame, OutputImageRawFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.local.tk import TkLocalTransport
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -27,6 +29,25 @@ load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
class MirrorProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, InputAudioRawFrame):
await self.push_frame(OutputAudioRawFrame(
audio=frame.audio,
sample_rate=frame.sample_rate,
num_channels=frame.num_channels)
)
elif isinstance(frame, InputImageRawFrame):
await self.push_frame(OutputImageRawFrame(
image=frame.image,
size=frame.size,
format=frame.format)
)
else:
await self.push_frame(frame, direction)
async def main():
async with aiohttp.ClientSession() as session:
@@ -52,7 +73,7 @@ async def main():
async def on_first_participant_joined(transport, participant):
transport.capture_participant_video(participant["id"])
pipeline = Pipeline([daily_transport.input(), tk_transport.output()])
pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
task = PipelineTask(pipeline)

6
examples/foundational/11-sound-effects.py
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@@ -12,9 +12,9 @@ import wave
from pipecat.frames.frames import (
Frame,
AudioRawFrame,
LLMFullResponseEndFrame,
LLMMessagesFrame,
OutputAudioRawFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
@@ -53,8 +53,8 @@ for file in sound_files:
filename = os.path.splitext(os.path.basename(full_path))[0]
# Open the image and convert it to bytes
with wave.open(full_path) as audio_file:
sounds[file] = AudioRawFrame(audio_file.readframes(-1),
audio_file.getframerate(), audio_file.getnchannels())
sounds[file] = OutputAudioRawFrame(audio_file.readframes(-1),
audio_file.getframerate(), audio_file.getnchannels())
class OutboundSoundEffectWrapper(FrameProcessor):

11
examples/moondream-chatbot/bot.py
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@@ -13,10 +13,11 @@ from PIL import Image
from pipecat.frames.frames import (
ImageRawFrame,
OutputImageRawFrame,
SpriteFrame,
Frame,
LLMMessagesFrame,
AudioRawFrame,
TTSAudioRawFrame,
TTSStoppedFrame,
TextFrame,
UserImageRawFrame,
@@ -59,7 +60,11 @@ for i in range(1, 26):
# Get the filename without the extension to use as the dictionary key
# Open the image and convert it to bytes
with Image.open(full_path) as img:
sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
sprites.append(OutputImageRawFrame(
image=img.tobytes(),
size=img.size,
format=img.format)
)
flipped = sprites[::-1]
sprites.extend(flipped)
@@ -82,7 +87,7 @@ class TalkingAnimation(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, AudioRawFrame):
if isinstance(frame, TTSAudioRawFrame):
if not self._is_talking:
await self.push_frame(talking_frame)
self._is_talking = True

2
examples/moondream-chatbot/requirements.txt
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@@ -1,4 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,moondream,openai,silero]
pipecat-ai[daily,cartesia,moondream,openai,silero]

7
examples/patient-intake/bot.py
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@@ -10,7 +10,7 @@ import os
import sys
import wave
from pipecat.frames.frames import AudioRawFrame
from pipecat.frames.frames import OutputAudioRawFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -49,8 +49,9 @@ for file in sound_files:
filename = os.path.splitext(os.path.basename(full_path))[0]
# Open the sound and convert it to bytes
with wave.open(full_path) as audio_file:
sounds[file] = AudioRawFrame(audio_file.readframes(-1),
audio_file.getframerate(), audio_file.getnchannels())
sounds[file] = OutputAudioRawFrame(audio_file.readframes(-1),
audio_file.getframerate(),
audio_file.getnchannels())
class IntakeProcessor:

2
examples/patient-intake/requirements.txt
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@@ -1,4 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero]
pipecat-ai[daily,cartesia,openai,silero]

12
examples/simple-chatbot/bot.py
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@@ -16,11 +16,11 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
from pipecat.frames.frames import (
AudioRawFrame,
ImageRawFrame,
OutputImageRawFrame,
SpriteFrame,
Frame,
LLMMessagesFrame,
TTSAudioRawFrame,
TTSStoppedFrame
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -49,7 +49,11 @@ for i in range(1, 26):
# Get the filename without the extension to use as the dictionary key
# Open the image and convert it to bytes
with Image.open(full_path) as img:
sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
sprites.append(OutputImageRawFrame(
image=img.tobytes(),
size=img.size,
format=img.format)
)
flipped = sprites[::-1]
sprites.extend(flipped)
@@ -72,7 +76,7 @@ class TalkingAnimation(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, AudioRawFrame):
if isinstance(frame, TTSAudioRawFrame):
if not self._is_talking:
await self.push_frame(talking_frame)
self._is_talking = True

2
examples/simple-chatbot/requirements.txt
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@@ -1,4 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero]
pipecat-ai[daily,elevenlabs,openai,silero]

2
examples/storytelling-chatbot/requirements.txt
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@@ -2,4 +2,4 @@ async_timeout
fastapi
uvicorn
python-dotenv
pipecat-ai[daily,openai,fal]
pipecat-ai[daily,elevenlabs,openai,fal]

11
examples/storytelling-chatbot/src/utils/helpers.py
View File

@@ -2,7 +2,7 @@ import os
import wave
from PIL import Image
from pipecat.frames.frames import AudioRawFrame, ImageRawFrame
from pipecat.frames.frames import OutputAudioRawFrame, OutputImageRawFrame
script_dir = os.path.dirname(__file__)
@@ -16,7 +16,8 @@ def load_images(image_files):
filename = os.path.splitext(os.path.basename(full_path))[0]
# Open the image and convert it to bytes
with Image.open(full_path) as img:
images[filename] = ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format)
images[filename] = OutputImageRawFrame(
image=img.tobytes(), size=img.size, format=img.format)
return images
@@ -30,8 +31,8 @@ def load_sounds(sound_files):
filename = os.path.splitext(os.path.basename(full_path))[0]
# Open the sound and convert it to bytes
with wave.open(full_path) as audio_file:
sounds[filename] = AudioRawFrame(audio=audio_file.readframes(-1),
sample_rate=audio_file.getframerate(),
num_channels=audio_file.getnchannels())
sounds[filename] = OutputAudioRawFrame(audio=audio_file.readframes(-1),
sample_rate=audio_file.getframerate(),
num_channels=audio_file.getnchannels())
return sounds

2
examples/twilio-chatbot/README.md
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@@ -55,7 +55,7 @@ This project is a FastAPI-based chatbot that integrates with Twilio to handle We
2. **Update the Twilio Webhook**:
Copy the ngrok URL and update your Twilio phone number webhook URL to `http://<ngrok_url>/start_call`.
3. **Update the streams.xml**:
3. **Update streams.xml**:
Copy the ngrok URL and update templates/streams.xml with `wss://<ngrok_url>/ws`.
## Running the Application

96
examples/twilio-chatbot/bot.py
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@@ -1,4 +1,3 @@
import aiohttp
import os
import sys
@@ -27,63 +26,62 @@ logger.add(sys.stderr, level="DEBUG")
async def run_bot(websocket_client, stream_sid):
async with aiohttp.ClientSession() as session:
transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
audio_out_enabled=True,
add_wav_header=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
serializer=TwilioFrameSerializer(stream_sid)
)
transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
audio_out_enabled=True,
add_wav_header=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
serializer=TwilioFrameSerializer(stream_sid)
)
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o")
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o")
stt = DeepgramSTTService(api_key=os.getenv('DEEPGRAM_API_KEY'))
stt = DeepgramSTTService(api_key=os.getenv('DEEPGRAM_API_KEY'))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
messages = [
{
"role": "system",
"content": "You are a helpful LLM in an audio call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
messages = [
{
"role": "system",
"content": "You are a helpful LLM in an audio call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
pipeline = Pipeline([
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out # LLM responses
])
pipeline = Pipeline([
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out # LLM responses
])
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
await task.queue_frames([EndFrame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
await task.queue_frames([EndFrame()])
runner = PipelineRunner(handle_sigint=False)
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
await runner.run(task)

2
examples/twilio-chatbot/requirements.txt
View File

@@ -1,4 +1,4 @@
pipecat-ai[daily,openai,silero,deepgram]
pipecat-ai[daily,cartesia,openai,silero,deepgram]
fastapi
uvicorn
python-dotenv

86
examples/websocket-server/bot.py
View File

@@ -4,7 +4,6 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import aiohttp
import asyncio
import os
import sys
@@ -33,60 +32,59 @@ logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
transport = WebsocketServerTransport(
params=WebsocketServerParams(
audio_out_enabled=True,
add_wav_header=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True
)
transport = WebsocketServerTransport(
params=WebsocketServerParams(
audio_out_enabled=True,
add_wav_header=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True
)
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o")
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
messages = [
{
"role": "system",
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
messages = [
{
"role": "system",
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
pipeline = Pipeline([
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out # LLM responses
])
pipeline = Pipeline([
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out # LLM responses
])
task = PipelineTask(pipeline)
task = PipelineTask(pipeline)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
runner = PipelineRunner()
runner = PipelineRunner()
await runner.run(task)
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

1
examples/websocket-server/frames.proto
View File

@@ -24,6 +24,7 @@ message AudioRawFrame {
bytes audio = 3;
uint32 sample_rate = 4;
uint32 num_channels = 5;
optional uint64 pts = 6;
}
message TranscriptionFrame {

2
examples/websocket-server/requirements.txt
View File

@@ -1,2 +1,2 @@
python-dotenv
pipecat-ai[openai,silero,websocket,whisper]
pipecat-ai[cartesia,openai,silero,websocket,whisper]

1
src/pipecat/frames/frames.proto
View File

@@ -24,6 +24,7 @@ message AudioRawFrame {
bytes audio = 3;
uint32 sample_rate = 4;
uint32 num_channels = 5;
optional uint64 pts = 6;
}
message TranscriptionFrame {

78
src/pipecat/frames/frames.py
View File

@@ -42,10 +42,7 @@ class DataFrame(Frame):
@dataclass
class AudioRawFrame(DataFrame):
"""A chunk of audio. Will be played by the transport if the transport's
microphone has been enabled.
"""
"""A chunk of audio."""
audio: bytes
sample_rate: int
num_channels: int
@@ -59,6 +56,31 @@ class AudioRawFrame(DataFrame):
return f"{self.name}(pts: {pts}, size: {len(self.audio)}, frames: {self.num_frames}, sample_rate: {self.sample_rate}, channels: {self.num_channels})"
@dataclass
class InputAudioRawFrame(AudioRawFrame):
"""A chunk of audio usually coming from an input transport.
"""
pass
@dataclass
class OutputAudioRawFrame(AudioRawFrame):
"""A chunk of audio. Will be played by the output transport if the
transport's microphone has been enabled.
"""
pass
@dataclass
class TTSAudioRawFrame(OutputAudioRawFrame):
"""A chunk of output audio generated by a TTS service.
"""
pass
@dataclass
class ImageRawFrame(DataFrame):
"""An image. Will be shown by the transport if the transport's camera is
@@ -75,20 +97,30 @@ class ImageRawFrame(DataFrame):
@dataclass
class URLImageRawFrame(ImageRawFrame):
"""An image with an associated URL. Will be shown by the transport if the
transport's camera is enabled.
"""
url: str | None
def __str__(self):
pts = format_pts(self.pts)
return f"{self.name}(pts: {pts}, url: {self.url}, size: {self.size}, format: {self.format})"
class InputImageRawFrame(ImageRawFrame):
pass
@dataclass
class VisionImageRawFrame(ImageRawFrame):
class OutputImageRawFrame(ImageRawFrame):
pass
@dataclass
class UserImageRawFrame(InputImageRawFrame):
"""An image associated to a user. Will be shown by the transport if the
transport's camera is enabled.
"""
user_id: str
def __str__(self):
pts = format_pts(self.pts)
return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format})"
@dataclass
class VisionImageRawFrame(InputImageRawFrame):
"""An image with an associated text to ask for a description of it. Will be
shown by the transport if the transport's camera is enabled.
@@ -101,16 +133,16 @@ class VisionImageRawFrame(ImageRawFrame):
@dataclass
class UserImageRawFrame(ImageRawFrame):
"""An image associated to a user. Will be shown by the transport if the
class URLImageRawFrame(OutputImageRawFrame):
"""An image with an associated URL. Will be shown by the transport if the
transport's camera is enabled.
"""
user_id: str
url: str | None
def __str__(self):
pts = format_pts(self.pts)
return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format})"
return f"{self.name}(pts: {pts}, url: {self.url}, size: {self.size}, format: {self.format})"
@dataclass
@@ -419,10 +451,10 @@ class BotSpeakingFrame(ControlFrame):
@dataclass
class TTSStartedFrame(ControlFrame):
"""Used to indicate the beginning of a TTS response. Following
AudioRawFrames are part of the TTS response until an TTSStoppedFrame. These
frames can be used for aggregating audio frames in a transport to optimize
the size of frames sent to the session, without needing to control this in
the TTS service.
TTSAudioRawFrames are part of the TTS response until an
TTSStoppedFrame. These frames can be used for aggregating audio frames in a
transport to optimize the size of frames sent to the session, without
needing to control this in the TTS service.
"""
pass

12
src/pipecat/frames/protobufs/frames_pb2.py
View File

@@ -14,7 +14,7 @@ _sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0c\x66rames.proto\x12\x07pipecat\"3\n\tTextFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0c\n\x04text\x18\x03 \x01(\t\"c\n\rAudioRawFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\r\n\x05\x61udio\x18\x03 \x01(\x0c\x12\x13\n\x0bsample_rate\x18\x04 \x01(\r\x12\x14\n\x0cnum_channels\x18\x05 \x01(\r\"`\n\x12TranscriptionFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0c\n\x04text\x18\x03 \x01(\t\x12\x0f\n\x07user_id\x18\x04 \x01(\t\x12\x11\n\ttimestamp\x18\x05 \x01(\t\"\x93\x01\n\x05\x46rame\x12\"\n\x04text\x18\x01 \x01(\x0b\x32\x12.pipecat.TextFrameH\x00\x12\'\n\x05\x61udio\x18\x02 \x01(\x0b\x32\x16.pipecat.AudioRawFrameH\x00\x12\x34\n\rtranscription\x18\x03 \x01(\x0b\x32\x1b.pipecat.TranscriptionFrameH\x00\x42\x07\n\x05\x66rameb\x06proto3')
DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0c\x66rames.proto\x12\x07pipecat\"3\n\tTextFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0c\n\x04text\x18\x03 \x01(\t\"}\n\rAudioRawFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\r\n\x05\x61udio\x18\x03 \x01(\x0c\x12\x13\n\x0bsample_rate\x18\x04 \x01(\r\x12\x14\n\x0cnum_channels\x18\x05 \x01(\r\x12\x10\n\x03pts\x18\x06 \x01(\x04H\x00\x88\x01\x01\x42\x06\n\x04_pts\"`\n\x12TranscriptionFrame\x12\n\n\x02id\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0c\n\x04text\x18\x03 \x01(\t\x12\x0f\n\x07user_id\x18\x04 \x01(\t\x12\x11\n\ttimestamp\x18\x05 \x01(\t\"\x93\x01\n\x05\x46rame\x12\"\n\x04text\x18\x01 \x01(\x0b\x32\x12.pipecat.TextFrameH\x00\x12\'\n\x05\x61udio\x18\x02 \x01(\x0b\x32\x16.pipecat.AudioRawFrameH\x00\x12\x34\n\rtranscription\x18\x03 \x01(\x0b\x32\x1b.pipecat.TranscriptionFrameH\x00\x42\x07\n\x05\x66rameb\x06proto3')
_globals = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -24,9 +24,9 @@ if _descriptor._USE_C_DESCRIPTORS == False:
_globals['_TEXTFRAME']._serialized_start=25
_globals['_TEXTFRAME']._serialized_end=76
_globals['_AUDIORAWFRAME']._serialized_start=78
_globals['_AUDIORAWFRAME']._serialized_end=177
_globals['_TRANSCRIPTIONFRAME']._serialized_start=179
_globals['_TRANSCRIPTIONFRAME']._serialized_end=275
_globals['_FRAME']._serialized_start=278
_globals['_FRAME']._serialized_end=425
_globals['_AUDIORAWFRAME']._serialized_end=203
_globals['_TRANSCRIPTIONFRAME']._serialized_start=205
_globals['_TRANSCRIPTIONFRAME']._serialized_end=301
_globals['_FRAME']._serialized_start=304
_globals['_FRAME']._serialized_end=451
# @@protoc_insertion_point(module_scope)

6
src/pipecat/processors/aggregators/llm_response.py
View File

@@ -4,7 +4,7 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
from typing import List
from typing import List, Type
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame, OpenAILLMContext
@@ -34,8 +34,8 @@ class LLMResponseAggregator(FrameProcessor):
role: str,
start_frame,
end_frame,
accumulator_frame: TextFrame,
interim_accumulator_frame: TextFrame | None = None,
accumulator_frame: Type[TextFrame],
interim_accumulator_frame: Type[TextFrame] | None = None,
handle_interruptions: bool = False
):
super().__init__()

6
src/pipecat/processors/aggregators/openai_llm_context.py
View File

@@ -13,7 +13,11 @@ from typing import Any, Awaitable, Callable, List
from PIL import Image
from pipecat.frames.frames import Frame, VisionImageRawFrame, FunctionCallInProgressFrame, FunctionCallResultFrame
from pipecat.frames.frames import (
Frame,
VisionImageRawFrame,
FunctionCallInProgressFrame,
FunctionCallResultFrame)
from pipecat.processors.frame_processor import FrameProcessor
from loguru import logger

12
src/pipecat/processors/aggregators/vision_image_frame.py
View File

@@ -4,13 +4,19 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
from pipecat.frames.frames import Frame, ImageRawFrame, TextFrame, VisionImageRawFrame
from pipecat.frames.frames import (
Frame,
InputImageRawFrame,
TextFrame,
VisionImageRawFrame
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
class VisionImageFrameAggregator(FrameProcessor):
"""This aggregator waits for a consecutive TextFrame and an
ImageFrame. After the ImageFrame arrives it will output a VisionImageFrame.
InputImageRawFrame. After the InputImageRawFrame arrives it will output a
VisionImageRawFrame.
>>> from pipecat.frames.frames import ImageFrame
@@ -34,7 +40,7 @@ class VisionImageFrameAggregator(FrameProcessor):
if isinstance(frame, TextFrame):
self._describe_text = frame.text
elif isinstance(frame, ImageRawFrame):
elif isinstance(frame, InputImageRawFrame):
if self._describe_text:
frame = VisionImageRawFrame(
text=self._describe_text,

12
src/pipecat/processors/gstreamer/pipeline_source.py
View File

@@ -9,11 +9,11 @@ import asyncio
from pydantic import BaseModel
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
Frame,
ImageRawFrame,
OutputAudioRawFrame,
OutputImageRawFrame,
StartFrame,
SystemFrame)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -182,9 +182,9 @@ class GStreamerPipelineSource(FrameProcessor):
def _appsink_audio_new_sample(self, appsink: GstApp.AppSink):
buffer = appsink.pull_sample().get_buffer()
(_, info) = buffer.map(Gst.MapFlags.READ)
frame = AudioRawFrame(audio=info.data,
sample_rate=self._out_params.audio_sample_rate,
num_channels=self._out_params.audio_channels)
frame = OutputAudioRawFrame(audio=info.data,
sample_rate=self._out_params.audio_sample_rate,
num_channels=self._out_params.audio_channels)
asyncio.run_coroutine_threadsafe(self.push_frame(frame), self.get_event_loop())
buffer.unmap(info)
return Gst.FlowReturn.OK
@@ -192,7 +192,7 @@ class GStreamerPipelineSource(FrameProcessor):
def _appsink_video_new_sample(self, appsink: GstApp.AppSink):
buffer = appsink.pull_sample().get_buffer()
(_, info) = buffer.map(Gst.MapFlags.READ)
frame = ImageRawFrame(
frame = OutputImageRawFrame(
image=info.data,
size=(self._out_params.video_width, self._out_params.video_height),
format="RGB")

13
src/pipecat/serializers/livekit.py
View File

@@ -7,7 +7,10 @@
import ctypes
import pickle
from pipecat.frames.frames import AudioRawFrame, Frame
from pipecat.frames.frames import (
Frame,
InputAudioRawFrame,
OutputAudioRawFrame)
from pipecat.serializers.base_serializer import FrameSerializer
from loguru import logger
@@ -22,12 +25,8 @@ except ModuleNotFoundError as e:
class LivekitFrameSerializer(FrameSerializer):
SERIALIZABLE_TYPES = {
AudioRawFrame: "audio",
}
def serialize(self, frame: Frame) -> str | bytes | None:
if not isinstance(frame, AudioRawFrame):
if not isinstance(frame, OutputAudioRawFrame):
return None
audio_frame = AudioFrame(
data=frame.audio,
@@ -39,7 +38,7 @@ class LivekitFrameSerializer(FrameSerializer):
def deserialize(self, data: str | bytes) -> Frame | None:
audio_frame: AudioFrame = pickle.loads(data)['frame']
return AudioRawFrame(
return InputAudioRawFrame(
audio=bytes(audio_frame.data),
sample_rate=audio_frame.sample_rate,
num_channels=audio_frame.num_channels,

24
src/pipecat/serializers/protobuf.py
View File

@@ -8,7 +8,11 @@ import dataclasses
import pipecat.frames.protobufs.frames_pb2 as frame_protos
from pipecat.frames.frames import AudioRawFrame, Frame, TextFrame, TranscriptionFrame
from pipecat.frames.frames import (
AudioRawFrame,
Frame,
TextFrame,
TranscriptionFrame)
from pipecat.serializers.base_serializer import FrameSerializer
from loguru import logger
@@ -29,14 +33,15 @@ class ProtobufFrameSerializer(FrameSerializer):
def serialize(self, frame: Frame) -> str | bytes | None:
proto_frame = frame_protos.Frame()
if type(frame) not in self.SERIALIZABLE_TYPES:
raise ValueError(
f"Frame type {type(frame)} is not serializable. You may need to add it to ProtobufFrameSerializer.SERIALIZABLE_FIELDS.")
logger.warning(f"Frame type {type(frame)} is not serializable")
return None
# ignoring linter errors; we check that type(frame) is in this dict above
proto_optional_name = self.SERIALIZABLE_TYPES[type(frame)] # type: ignore
for field in dataclasses.fields(frame): # type: ignore
setattr(getattr(proto_frame, proto_optional_name), field.name,
getattr(frame, field.name))
value = getattr(frame, field.name)
if value:
setattr(getattr(proto_frame, proto_optional_name), field.name, value)
result = proto_frame.SerializeToString()
return result
@@ -48,8 +53,8 @@ class ProtobufFrameSerializer(FrameSerializer):
>>> serializer = ProtobufFrameSerializer()
>>> serializer.deserialize(
... serializer.serialize(AudioFrame(data=b'1234567890')))
AudioFrame(data=b'1234567890')
... serializer.serialize(OutputAudioFrame(data=b'1234567890')))
InputAudioFrame(data=b'1234567890')
>>> serializer.deserialize(
... serializer.serialize(TextFrame(text='hello world')))
@@ -75,10 +80,13 @@ class ProtobufFrameSerializer(FrameSerializer):
# Remove special fields if needed
id = getattr(args, "id")
name = getattr(args, "name")
pts = getattr(args, "pts")
if not id:
del args_dict["id"]
if not name:
del args_dict["name"]
if not pts:
del args_dict["pts"]
# Create the instance
instance = class_name(**args_dict)
@@ -88,5 +96,7 @@ class ProtobufFrameSerializer(FrameSerializer):
setattr(instance, "id", getattr(args, "id"))
if name:
setattr(instance, "name", getattr(args, "name"))
if pts:
setattr(instance, "pts", getattr(args, "pts"))
return instance

9
src/pipecat/serializers/twilio.py
View File

@@ -9,7 +9,10 @@ import json
from pydantic import BaseModel
from pipecat.frames.frames import AudioRawFrame, Frame, StartInterruptionFrame
from pipecat.frames.frames import (
AudioRawFrame,
Frame,
StartInterruptionFrame)
from pipecat.serializers.base_serializer import FrameSerializer
from pipecat.utils.audio import ulaw_to_pcm, pcm_to_ulaw
@@ -19,10 +22,6 @@ class TwilioFrameSerializer(FrameSerializer):
twilio_sample_rate: int = 8000
sample_rate: int = 16000
SERIALIZABLE_TYPES = {
AudioRawFrame: "audio",
}
def __init__(self, stream_sid: str, params: InputParams = InputParams()):
self._stream_sid = stream_sid
self._params = params

3
src/pipecat/services/ai_services.py
View File

@@ -22,6 +22,7 @@ from pipecat.frames.frames import (
STTModelUpdateFrame,
StartFrame,
StartInterruptionFrame,
TTSAudioRawFrame,
TTSLanguageUpdateFrame,
TTSModelUpdateFrame,
TTSSpeakFrame,
@@ -287,7 +288,7 @@ class AsyncTTSService(TTSService):
if self._push_stop_frames and (
isinstance(frame, StartInterruptionFrame) or
isinstance(frame, TTSStartedFrame) or
isinstance(frame, AudioRawFrame) or
isinstance(frame, TTSAudioRawFrame) or
isinstance(frame, TTSStoppedFrame)):
await self._stop_frame_queue.put(frame)

4
src/pipecat/services/azure.py
View File

@@ -12,12 +12,12 @@ from PIL import Image
from typing import AsyncGenerator
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
StartFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
TranscriptionFrame,
@@ -117,7 +117,7 @@ class AzureTTSService(TTSService):
await self.stop_ttfb_metrics()
await self.push_frame(TTSStartedFrame())
# Azure always sends a 44-byte header. Strip it off.
yield AudioRawFrame(audio=result.audio_data[44:], sample_rate=self._sample_rate, num_channels=1)
yield TTSAudioRawFrame(audio=result.audio_data[44:], sample_rate=self._sample_rate, num_channels=1)
await self.push_frame(TTSStoppedFrame())
elif result.reason == ResultReason.Canceled:
cancellation_details = result.cancellation_details

6
src/pipecat/services/cartesia.py
View File

@@ -15,10 +15,10 @@ from pipecat.frames.frames import (
CancelFrame,
ErrorFrame,
Frame,
AudioRawFrame,
StartInterruptionFrame,
StartFrame,
EndFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
LLMFullResponseEndFrame
@@ -206,7 +206,7 @@ class CartesiaTTSService(AsyncWordTTSService):
elif msg["type"] == "chunk":
await self.stop_ttfb_metrics()
self.start_word_timestamps()
frame = AudioRawFrame(
frame = TTSAudioRawFrame(
audio=base64.b64decode(msg["data"]),
sample_rate=self._output_format["sample_rate"],
num_channels=1
@@ -331,7 +331,7 @@ class CartesiaHttpTTSService(TTSService):
await self.stop_ttfb_metrics()
frame = AudioRawFrame(
frame = TTSAudioRawFrame(
audio=output["audio"],
sample_rate=self._output_format["sample_rate"],
num_channels=1

5
src/pipecat/services/deepgram.py
View File

@@ -9,13 +9,13 @@ import aiohttp
from typing import AsyncGenerator
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
InterimTranscriptionFrame,
StartFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
TranscriptionFrame)
@@ -101,7 +101,8 @@ class DeepgramTTSService(TTSService):
await self.push_frame(TTSStartedFrame())
async for data in r.content:
await self.stop_ttfb_metrics()
frame = AudioRawFrame(audio=data, sample_rate=self._sample_rate, num_channels=1)
frame = TTSAudioRawFrame(
audio=data, sample_rate=self._sample_rate, num_channels=1)
yield frame
await self.push_frame(TTSStoppedFrame())
except Exception as e:

4
src/pipecat/services/elevenlabs.py
View File

@@ -12,12 +12,12 @@ from typing import Any, AsyncGenerator, List, Literal, Mapping, Tuple
from pydantic import BaseModel
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
Frame,
StartFrame,
StartInterruptionFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame)
from pipecat.processors.frame_processor import FrameDirection
@@ -209,7 +209,7 @@ class ElevenLabsTTSService(AsyncWordTTSService):
self.start_word_timestamps()
audio = base64.b64decode(msg["audio"])
frame = AudioRawFrame(audio, self.sample_rate, 1)
frame = TTSAudioRawFrame(audio, self.sample_rate, 1)
await self.push_frame(frame)
if msg.get("alignment"):

4
src/pipecat/services/lmnt.py
View File

@@ -10,13 +10,13 @@ from typing import AsyncGenerator
from pipecat.processors.frame_processor import FrameDirection
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
StartFrame,
StartInterruptionFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
)
@@ -126,7 +126,7 @@ class LmntTTSService(AsyncTTSService):
await self.push_error(ErrorFrame(f'{self} error: {msg["error"]}'))
elif "audio" in msg:
await self.stop_ttfb_metrics()
frame = AudioRawFrame(
frame = TTSAudioRawFrame(
audio=msg["audio"],
sample_rate=self._output_format["sample_rate"],
num_channels=1

4
src/pipecat/services/openai.py
View File

@@ -17,13 +17,13 @@ from loguru import logger
from PIL import Image
from pipecat.frames.frames import (
AudioRawFrame,
ErrorFrame,
Frame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMMessagesFrame,
LLMModelUpdateFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
TextFrame,
@@ -364,7 +364,7 @@ class OpenAITTSService(TTSService):
async for chunk in r.iter_bytes(8192):
if len(chunk) > 0:
await self.stop_ttfb_metrics()
frame = AudioRawFrame(chunk, self.sample_rate, 1)
frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
yield frame
await self.push_frame(TTSStoppedFrame())
except BadRequestError as e:

8
src/pipecat/services/playht.py
View File

@@ -9,7 +9,11 @@ import struct
from typing import AsyncGenerator
from pipecat.frames.frames import AudioRawFrame, Frame, TTSStartedFrame, TTSStoppedFrame
from pipecat.frames.frames import (
Frame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame)
from pipecat.services.ai_services import TTSService
from loguru import logger
@@ -91,7 +95,7 @@ class PlayHTTTSService(TTSService):
else:
if len(chunk):
await self.stop_ttfb_metrics()
frame = AudioRawFrame(chunk, 16000, 1)
frame = TTSAudioRawFrame(chunk, 16000, 1)
yield frame
await self.push_frame(TTSStoppedFrame())
except Exception as e:

10
src/pipecat/services/together.py
View File

@@ -4,16 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import base64
import json
import io
import copy
from typing import List, Optional
from dataclasses import dataclass
from asyncio import CancelledError
import re
import uuid
from typing import List
from dataclasses import dataclass
from asyncio import CancelledError
from pipecat.frames.frames import (
Frame,
LLMModelUpdateFrame,

6
src/pipecat/services/xtts.py
View File

@@ -9,10 +9,10 @@ import aiohttp
from typing import Any, AsyncGenerator, Dict
from pipecat.frames.frames import (
AudioRawFrame,
ErrorFrame,
Frame,
StartFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame)
from pipecat.services.ai_services import TTSService
@@ -128,7 +128,7 @@ class XTTSService(TTSService):
# Convert the numpy array back to bytes
resampled_audio_bytes = resampled_audio.astype(np.int16).tobytes()
# Create the frame with the resampled audio
frame = AudioRawFrame(resampled_audio_bytes, 16000, 1)
frame = TTSAudioRawFrame(resampled_audio_bytes, 16000, 1)
yield frame
# Process any remaining data in the buffer
@@ -136,7 +136,7 @@ class XTTSService(TTSService):
audio_np = np.frombuffer(buffer, dtype=np.int16)
resampled_audio = resampy.resample(audio_np, 24000, 16000)
resampled_audio_bytes = resampled_audio.astype(np.int16).tobytes()
frame = AudioRawFrame(resampled_audio_bytes, 16000, 1)
frame = TTSAudioRawFrame(resampled_audio_bytes, 16000, 1)
yield frame
await self.push_frame(TTSStoppedFrame())

6
src/pipecat/transports/base_input.py
View File

@@ -10,9 +10,9 @@ from concurrent.futures import ThreadPoolExecutor
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.frames.frames import (
AudioRawFrame,
BotInterruptionFrame,
CancelFrame,
InputAudioRawFrame,
StartFrame,
EndFrame,
Frame,
@@ -59,7 +59,7 @@ class BaseInputTransport(FrameProcessor):
def vad_analyzer(self) -> VADAnalyzer | None:
return self._params.vad_analyzer
async def push_audio_frame(self, frame: AudioRawFrame):
async def push_audio_frame(self, frame: InputAudioRawFrame):
if self._params.audio_in_enabled or self._params.vad_enabled:
await self._audio_in_queue.put(frame)
@@ -151,7 +151,7 @@ class BaseInputTransport(FrameProcessor):
vad_state: VADState = VADState.QUIET
while True:
try:
frame: AudioRawFrame = await self._audio_in_queue.get()
frame: InputAudioRawFrame = await self._audio_in_queue.get()
audio_passthrough = True

39
src/pipecat/transports/base_output.py
View File

@@ -15,17 +15,17 @@ from typing import List
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.frames.frames import (
AudioRawFrame,
BotSpeakingFrame,
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
CancelFrame,
MetricsFrame,
OutputAudioRawFrame,
OutputImageRawFrame,
SpriteFrame,
StartFrame,
EndFrame,
Frame,
ImageRawFrame,
StartInterruptionFrame,
StopInterruptionFrame,
SystemFrame,
@@ -122,7 +122,7 @@ class BaseOutputTransport(FrameProcessor):
async def send_metrics(self, frame: MetricsFrame):
pass
async def write_frame_to_camera(self, frame: ImageRawFrame):
async def write_frame_to_camera(self, frame: OutputImageRawFrame):
pass
async def write_raw_audio_frames(self, frames: bytes):
@@ -162,9 +162,9 @@ class BaseOutputTransport(FrameProcessor):
await self._sink_queue.put(frame)
await self.stop(frame)
# Other frames.
elif isinstance(frame, AudioRawFrame):
elif isinstance(frame, OutputAudioRawFrame):
await self._handle_audio(frame)
elif isinstance(frame, ImageRawFrame) or isinstance(frame, SpriteFrame):
elif isinstance(frame, OutputImageRawFrame) or isinstance(frame, SpriteFrame):
await self._handle_image(frame)
elif isinstance(frame, TransportMessageFrame) and frame.urgent:
await self.send_message(frame)
@@ -191,7 +191,7 @@ class BaseOutputTransport(FrameProcessor):
if self._bot_speaking:
await self._bot_stopped_speaking()
async def _handle_audio(self, frame: AudioRawFrame):
async def _handle_audio(self, frame: OutputAudioRawFrame):
if not self._params.audio_out_enabled:
return
@@ -200,12 +200,14 @@ class BaseOutputTransport(FrameProcessor):
else:
self._audio_buffer.extend(frame.audio)
while len(self._audio_buffer) >= self._audio_chunk_size:
chunk = AudioRawFrame(bytes(self._audio_buffer[:self._audio_chunk_size]),
sample_rate=frame.sample_rate, num_channels=frame.num_channels)
chunk = OutputAudioRawFrame(
bytes(self._audio_buffer[:self._audio_chunk_size]),
sample_rate=frame.sample_rate, num_channels=frame.num_channels
)
await self._sink_queue.put(chunk)
self._audio_buffer = self._audio_buffer[self._audio_chunk_size:]
async def _handle_image(self, frame: ImageRawFrame | SpriteFrame):
async def _handle_image(self, frame: OutputImageRawFrame | SpriteFrame):
if not self._params.camera_out_enabled:
return
@@ -226,11 +228,11 @@ class BaseOutputTransport(FrameProcessor):
self._sink_clock_task = loop.create_task(self._sink_clock_task_handler())
async def _sink_frame_handler(self, frame: Frame):
if isinstance(frame, AudioRawFrame):
if isinstance(frame, OutputAudioRawFrame):
await self.write_raw_audio_frames(frame.audio)
await self.push_frame(frame)
await self.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
elif isinstance(frame, ImageRawFrame):
elif isinstance(frame, OutputImageRawFrame):
await self._set_camera_image(frame)
elif isinstance(frame, SpriteFrame):
await self._set_camera_images(frame.images)
@@ -305,10 +307,10 @@ class BaseOutputTransport(FrameProcessor):
# Camera out
#
async def send_image(self, frame: ImageRawFrame | SpriteFrame):
async def send_image(self, frame: OutputImageRawFrame | SpriteFrame):
await self.process_frame(frame, FrameDirection.DOWNSTREAM)
async def _draw_image(self, frame: ImageRawFrame):
async def _draw_image(self, frame: OutputImageRawFrame):
desired_size = (self._params.camera_out_width, self._params.camera_out_height)
if frame.size != desired_size:
@@ -316,14 +318,17 @@ class BaseOutputTransport(FrameProcessor):
resized_image = image.resize(desired_size)
logger.warning(
f"{frame} does not have the expected size {desired_size}, resizing")
frame = ImageRawFrame(resized_image.tobytes(), resized_image.size, resized_image.format)
frame = OutputImageRawFrame(
resized_image.tobytes(),
resized_image.size,
resized_image.format)
await self.write_frame_to_camera(frame)
async def _set_camera_image(self, image: ImageRawFrame):
async def _set_camera_image(self, image: OutputImageRawFrame):
self._camera_images = itertools.cycle([image])
async def _set_camera_images(self, images: List[ImageRawFrame]):
async def _set_camera_images(self, images: List[OutputImageRawFrame]):
self._camera_images = itertools.cycle(images)
async def _camera_out_task_handler(self):
@@ -375,7 +380,7 @@ class BaseOutputTransport(FrameProcessor):
# Audio out
#
async def send_audio(self, frame: AudioRawFrame):
async def send_audio(self, frame: OutputAudioRawFrame):
await self.process_frame(frame, FrameDirection.DOWNSTREAM)
async def _audio_out_task_handler(self):

8
src/pipecat/transports/local/audio.py
View File

@@ -8,7 +8,7 @@ import asyncio
from concurrent.futures import ThreadPoolExecutor
from pipecat.frames.frames import AudioRawFrame, StartFrame
from pipecat.frames.frames import InputAudioRawFrame, StartFrame
from pipecat.processors.frame_processor import FrameProcessor
from pipecat.transports.base_input import BaseInputTransport
from pipecat.transports.base_output import BaseOutputTransport
@@ -54,9 +54,9 @@ class LocalAudioInputTransport(BaseInputTransport):
self._in_stream.close()
def _audio_in_callback(self, in_data, frame_count, time_info, status):
frame = AudioRawFrame(audio=in_data,
sample_rate=self._params.audio_in_sample_rate,
num_channels=self._params.audio_in_channels)
frame = InputAudioRawFrame(audio=in_data,
sample_rate=self._params.audio_in_sample_rate,
num_channels=self._params.audio_in_channels)
asyncio.run_coroutine_threadsafe(self.push_audio_frame(frame), self.get_event_loop())

13
src/pipecat/transports/local/tk.py
View File

@@ -11,8 +11,7 @@ from concurrent.futures import ThreadPoolExecutor
import numpy as np
import tkinter as tk
from pipecat.frames.frames import AudioRawFrame, ImageRawFrame, StartFrame
from pipecat.processors.frame_processor import FrameProcessor
from pipecat.frames.frames import InputAudioRawFrame, OutputImageRawFrame, StartFrame
from pipecat.transports.base_input import BaseInputTransport
from pipecat.transports.base_output import BaseOutputTransport
from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -64,9 +63,9 @@ class TkInputTransport(BaseInputTransport):
self._in_stream.close()
def _audio_in_callback(self, in_data, frame_count, time_info, status):
frame = AudioRawFrame(audio=in_data,
sample_rate=self._params.audio_in_sample_rate,
num_channels=self._params.audio_in_channels)
frame = InputAudioRawFrame(audio=in_data,
sample_rate=self._params.audio_in_sample_rate,
num_channels=self._params.audio_in_channels)
asyncio.run_coroutine_threadsafe(self.push_audio_frame(frame), self.get_event_loop())
@@ -108,10 +107,10 @@ class TkOutputTransport(BaseOutputTransport):
async def write_raw_audio_frames(self, frames: bytes):
await self.get_event_loop().run_in_executor(self._executor, self._out_stream.write, frames)
async def write_frame_to_camera(self, frame: ImageRawFrame):
async def write_frame_to_camera(self, frame: OutputImageRawFrame):
self.get_event_loop().call_soon(self._write_frame_to_tk, frame)
def _write_frame_to_tk(self, frame: ImageRawFrame):
def _write_frame_to_tk(self, frame: OutputImageRawFrame):
width = frame.size[0]
height = frame.size[1]
data = f"P6 {width} {height} 255 ".encode() + frame.image

18
src/pipecat/transports/network/fastapi_websocket.py
View File

@@ -12,8 +12,16 @@ import wave
from typing import Awaitable, Callable
from pydantic.main import BaseModel
from pipecat.frames.frames import AudioRawFrame, CancelFrame, EndFrame, Frame, StartFrame, StartInterruptionFrame
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
Frame,
InputAudioRawFrame,
StartFrame,
StartInterruptionFrame
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.serializers.base_serializer import FrameSerializer
from pipecat.transports.base_input import BaseInputTransport
from pipecat.transports.base_output import BaseOutputTransport
@@ -79,7 +87,11 @@ class FastAPIWebsocketInputTransport(BaseInputTransport):
continue
if isinstance(frame, AudioRawFrame):
await self.push_audio_frame(frame)
await self.push_audio_frame(InputAudioRawFrame(
audio=frame.audio,
sample_rate=frame.sample_rate,
num_channels=frame.num_channels)
)
await self._callbacks.on_client_disconnected(self._websocket)

9
src/pipecat/transports/network/websocket_server.py
View File

@@ -11,8 +11,7 @@ import wave
from typing import Awaitable, Callable
from pydantic.main import BaseModel
from pipecat.frames.frames import AudioRawFrame, CancelFrame, EndFrame, StartFrame
from pipecat.processors.frame_processor import FrameProcessor
from pipecat.frames.frames import AudioRawFrame, CancelFrame, EndFrame, InputAudioRawFrame, StartFrame
from pipecat.serializers.base_serializer import FrameSerializer
from pipecat.serializers.protobuf import ProtobufFrameSerializer
from pipecat.transports.base_input import BaseInputTransport
@@ -98,7 +97,11 @@ class WebsocketServerInputTransport(BaseInputTransport):
continue
if isinstance(frame, AudioRawFrame):
await self.queue_audio_frame(frame)
await self.push_audio_frame(InputAudioRawFrame(
audio=frame.audio,
sample_rate=frame.sample_rate,
num_channels=frame.num_channels)
)
else:
await self.push_frame(frame)

20
src/pipecat/transports/services/daily.py
View File

@@ -22,13 +22,14 @@ from daily import (
from pydantic.main import BaseModel
from pipecat.frames.frames import (
AudioRawFrame,
CancelFrame,
EndFrame,
Frame,
ImageRawFrame,
InputAudioRawFrame,
InterimTranscriptionFrame,
MetricsFrame,
OutputAudioRawFrame,
OutputImageRawFrame,
SpriteFrame,
StartFrame,
TranscriptionFrame,
@@ -240,7 +241,7 @@ class DailyTransportClient(EventHandler):
completion=completion_callback(future))
await future
async def read_next_audio_frame(self) -> AudioRawFrame | None:
async def read_next_audio_frame(self) -> InputAudioRawFrame | None:
if not self._speaker:
return None
@@ -253,7 +254,10 @@ class DailyTransportClient(EventHandler):
audio = await future
if len(audio) > 0:
return AudioRawFrame(audio=audio, sample_rate=sample_rate, num_channels=num_channels)
return InputAudioRawFrame(
audio=audio,
sample_rate=sample_rate,
num_channels=num_channels)
else:
# If we don't read any audio it could be there's no participant
# connected. daily-python will return immediately if that's the
@@ -269,7 +273,7 @@ class DailyTransportClient(EventHandler):
self._mic.write_frames(frames, completion=completion_callback(future))
await future
async def write_frame_to_camera(self, frame: ImageRawFrame):
async def write_frame_to_camera(self, frame: OutputImageRawFrame):
if not self._camera:
return None
@@ -759,7 +763,7 @@ class DailyOutputTransport(BaseOutputTransport):
async def write_raw_audio_frames(self, frames: bytes):
await self._client.write_raw_audio_frames(frames)
async def write_frame_to_camera(self, frame: ImageRawFrame):
async def write_frame_to_camera(self, frame: OutputImageRawFrame):
await self._client.write_frame_to_camera(frame)
@@ -839,11 +843,11 @@ class DailyTransport(BaseTransport):
def participant_id(self) -> str:
return self._client.participant_id
async def send_image(self, frame: ImageRawFrame | SpriteFrame):
async def send_image(self, frame: OutputImageRawFrame | SpriteFrame):
if self._output:
await self._output.process_frame(frame, FrameDirection.DOWNSTREAM)
async def send_audio(self, frame: AudioRawFrame):
async def send_audio(self, frame: OutputAudioRawFrame):
if self._output:
await self._output.process_frame(frame, FrameDirection.DOWNSTREAM)